Artificial Intelligence in PV System

Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

Solar ship, which integrates the solar photovoltaic (PV) system into its own ship power system, is becoming one kinds of most promising and fastest developing green ship. Since the electric energy transformed by PV system is provided to the power load through the ship's distribution system, the output power of synchronous generator will be reduced in proportion. Especially, if the matching relationship among solar PV system, energy storage system and the power load requirement is precisely analyzed and the operation characteristics and reliability of solar inverter have already meet the strict test standards for marine equipment, the design capacity of synchronous generators could also be reduced. The off-grid mode and grid mode are the main way of integrated application of solar power photovoltaic system of ship. And the solar PV system is a kind of inverter power supply, which is essentially based on the electronic inverted technology, so PV system can be regarded as a ‘fragile power source’ with ‘zero inertia’. Therefore, the influence of solar photovoltaic grid-connected system to the power flow and power quality of the ship power system cannot be ignored. And the effect of photovoltaic system on the power quality of transient and steady-state and flow calculation remains to be studied. This paper mainly research the changes of power load flow calculation(PLFC) on the system, using the toolbox PSAT of MATLAB Simulink. This paper research the PLFC on the system of different solar photovoltaic systems access points, and analysis the experimental results. The results show that, under the premise of the normal operation of the ship electric power system, the improvement of the capacity of solar photovoltaic system in ship , and different access modes of photovoltaic grid-connected system, have slight impact on the power flow of power system, indicating that the photovoltaic grid-connected system in ship is stable, an appropriate increase in the capacity of PV system is feasible. The results is of certain significance of secure application of grid connected photovoltaic system in this paper.

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications.

Artificial intelligence techniques for sizing photovoltaic systems: A review

This chapter presents a brief survey on the recent applications of artificial intelligence (AI) techniques in fault diagnosis of photovoltaic (PV) systems. AI-based methods are mainly used to identify and classify the type of faults that can happen in PV systems, particularly in DC side (PV array). The methods will be presented and discussed in terms of complexity implementation, possible faults detection, identification and localization capability. Faults localization in large-scale PV plants remains challenging issue, and to date, no AI-based method was applied and verified experimentally, except few methods recently developed based on aerial images (Infrared Thermography) inspection. It is believed that this chapter can help researchers in academic institutions to get an idea regarding the actual application of AI techniques in this topic.KeywordsPhotovoltaic plantsArtificial intelligence techniquesMachine learningDeep learningFault diagnosis

Uncertain renewable energy supplies, load demands and the non-linear characteristics of some components of photovoltaic (PV) systems make the design problem not easy to solve by classical optimization methods, especially when relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing PV systems. However, simple methods like worst month method are still largely used in sizing simple PV systems. In the present study, a method for sizing remote PV systems based on genetic algorithms has been compared with two classical methods, worst month method and loss of power supply probability (LPSP) method. The three methods have been applied to a PV lighting system with orientation due south and inclination angles between 0° and 90° in Adrar city (south Algeria). Because measured data for the chosen location were not available, a year of synthetic hourly meteorological data of this location, generated by PVSYST software, have been used in the simulation. Genetic algorithms and worst month methods give results close to each other between 0° and 60° but the system is largely oversized by the worst month method when the tilted angle is over 60°. The results obtained by LPSP method show that the system is very undersized. Hence, a proposition has been made to improve results obtained by this method.

It is no doubt that now a day’s Artificial intelligence (AI) is anemergent technology in the digital marketing industry. Commercial Artificial Intelligence (AI) is an important topic for research. Artificial intelligence is growing quickly more sensitive and popular in the digital marketing field due to the capacity of advertisers to employ it efficiently. In order to provide the capability of the highest level of originality and efficiency in digital marketing ground, artificial intelligence techniques are more fruitful for customers, marketers and, retailers. This paper depicts the impact of artificial intelligence techniques on advertising on a diminutive and average scale in the digital marketing field. Ad targeting, predictive analysis, voice search are different artificial intelligence techniques for digital marketing purposes throughout the consumer life cycle. The topic of Artificial Intelligence (AI) in digital marketing itself can be outsized enough to verifyeliminating the area of research into precise application and application frameworks.However, our objective in this paper is to provide a comparative analysis of the digital marketing field using artificial intelligence techniques and to propose some specific guidelines based on different parameters, tools, and frameworks.Through this paper, we have observed thelast five years (2016-2021) research trends which reflect that out of different parameters, customer relationship and promotional activity are the most widely used parameter in the digital marketing field. Another significant point is, most of the research identifies that artificial intelligence is a compulsory technique used in the modern digital marketing field. Some data collection and data analysis tools are also being used in this field using artificial intelligence techniques. Our research methods try to get a complete view on artificial intelligence (AI) Marketing were multifaceted but were very focused on secondary research sources. Finally in this paper, we propose some recommendationsthat will offer better Artificial Intelligence (AI) implementation in the digital marketing field.

Analysis of the operational performance and efficiency characteristic for photovoltaic system in Hong Kong

The best alternative to the conventional sources of energy is ever lasting solar energy and it is one among the cheapest and widely used energy. The present paper deals with the design and analysis of zeta converters, as it has been used as intermediate between voltage source inverter (VSI) and solar PV array. Maximum power point tracking (MPPT) technique has been used for gaining maximum efficiency from solar PV array. In this paper, Artificial Intelligence(AI) Technique based Fuzzy logic controller is used for better results. When compared to other methods of MPPT technique, Fuzzy logic based MPPT technique is best because it provides better results for randomly varying atmospheric conditions. In our proposed system, BLDC motor which has higher efficiency and noise less operation is considered. Maximum power loss of PV generator is well matched with the load characteristic of BLDC motor. Matlab/Simulink results carried out for the different topology of the DC-DC converters and the results are analyzed and compared. Keywords: Photovoltaic(PV) system, Maximum power point tracking (MPPT), Artificial Intelligence (AI) Technique, Fuzzy logic controller, BLDC motor, zeta converters.

Optimum battery design for applications in photovoltaic systems — theoretical considerations

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

Long term supporting schemes for photovoltaic (PV) system installation have led to accommodating large numbers of PV systems within load pockets in distribution grids. High penetrations of PV systems can cause new technical challenges, such as voltage rise due to reverse power flow during light load and high PV generation conditions. Therefore, new strategies are required to address the associated challenges. Moreover, due to these changes in distribution grids, a different response behavior of the distribution grid on the transmission side can be expected. Hence, a new equivalent model of distribution grids with high penetration of PV systems is needed to be addressed for future power system studies. The thesis contributions lie in three parts. The first part of the thesis copes with the PV modelling. A non-proprietary PV model of a three-phase, single stage PV system is developed in PSCAD/EMTDC and PowerFactory. Three different reactive power regulation strategies are incorporated into the models and their behavior are investigated in both simulation platforms using a distribution system with PV systems. In the second part of the thesis, the voltage rise problem is remedied by use of reactive power. On the other hand, considering large numbers of PV systems in grids, unnecessary reactive power consumption by PV systems first increases total line losses, and second it may also jeopardize the stability of the network in the case of contingencies in conventional power plants, which supply reactive power. Thus, this thesis investigates and develops the novel schemes to reduce reactive power flows while still keeping voltage within designated limits via three different approaches: 1. decentralized voltage control to the pre-defined set-points 2. developing a coordinated active power dependent (APD) voltage regulation Q(P) using local signals 3. developing a multi-objective coordinated droop-based voltage (DBV) regulation Q(V) using local signals In the third part of the thesis, furthermore, a gray-box load modeling is used to develop a new static equivalent model of a complex distribution grid with large numbers of PV systems embedded with voltage support schemes. In the proposed model, variations of voltage at the connection point simulate variations of the model’s active and reactive power. This model can simply be integrated into load-flow programs and replace the complex distribution grid, while still keeping the overall accuracy high. The thesis results, in conclusion, demonstrate: i) using rms-based simulations in PowerFactory can provide us with quite similar results using the time domain instantaneous values in PSCAD platform; ii) decentralized voltage control to specific setpoints through the PV systems in the distribution grid is fundamentally impossible due to the high level voltage control interaction and directionality among the PV systems; iii) the proposed APD method can regulate the voltage under the steady-state voltage limit and consume less total reactive power in contrast to the standard characteristic Cosf (P) proposed by German Grid Codes; iv) the proposed optimized DBV method can directly address voltage and successfully regulate it to the upper steady-state voltage limit by causing minimum reactive power consumption as well as line losses; v) it is beneficial to address PV systems as a separate entity in the equivalencing of distribution grids with high density of PV systems.

The integration of photovoltaic (PV) arrays to the power grid has continued to increase globally. Large PV array installations in the order of 100s of megawatt have already been installed. One of such large PV array installations is the Topaz PV array with an installed capacity of 500MW. With the increasing penetration of PV arrays, it is important to develop an accurate model for PV arrays. Such model will aid in performing state estimation, condition monitoring, load flow analysis, and other analytical studies. Several models for the PV module have been proposed in various publications. However, these models are written in a form that is effective for analyzing the PV module as an individual device. To analyze the PV array in an integrated power system, the form of the PV array model needs to be modified. This modification enables the establishment of connectivity between power system devices, and the definition of the admittance between the buses of the power system network. Consequently, computations such as load flow analysis and state estimation can be performed seamlessly. In this paper, a novel method for modeling a two-diode PV module is proposed. This model applies a scaling factor to the Taylor series expansion of the PV array model exponential terms. Next, the higher order terms of series expansion is reduced to at most second order terms using quadratization technique. This modeling method can also be applied to other PV array models, such as the one-diode and three-diode models. The form of the proposed PV array model enables seamless simulation of the PV array system in an integrated power system network.

Solar photovoltaic (PV) energy; latest developments in the building integrated and hybrid PV systems

The increasing adoption of renewable energy for electricity generation has led to a growing application of photovoltaic (PV) systems in residential and commercial settings across Nigeria. This study aims to provide a comprehensive review of the factors affecting the efficiency and output of PV systems applied in the tropical climate of South-South Nigeria. The environmental benefits of PV systems further enhance their appeal as a direct method of converting solar energy into electricity. For remote communities in South-South Nigeria, often disconnected from the national grid, PV systems offer a viable alternative electricity source. However, the efficiency and output of PV systems are significantly influenced by various environmental conditions prevalent in the tropical climate of South-South Nigeria. The methodology employed in this study includes an extensive literature review and analysis of PV system installations in the region. Data was collected from various sources, including journal articles, conference proceedings, and reports, to identify the key factors impacting PV system performance in South-South Nigeria. The findings highlight that high temperatures, humidity levels, frequent dust accumulation, and potential sea salt effects due to coastal proximity are among the critical environmental factors affecting PV system efficiency in this tropical zone. The study concludes that while the region's climate provides advantages for solar energy harvesting, it also presents unique challenges that need to be addressed when implementing PV systems in South-South Nigeria. The authors recommend strategies to mitigate the environmental challenges faced by PV systems in this tropical climate, contributing to the optimization of solar energy utilization in the region.